Large data sets may exist in various sizes and organizational structures. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. For example, billions of records (also referred to as rows) and hundreds of thousands of columns worth of data may populate a single table. Users and business processes may interact with the data sets by invoking data services. Typically, business processes may consume multiple data services as part of each business process flow, and each data service may be executed independently of the other corresponding data services. In the event of a partial failure of any data service, the failed data service operations may be retried and/or re-executed after a predefined time interval (e.g., one day). This could lead to data integrity issues as some of the data services are successful, while others fail.